Image dataset for detecting sugarcane white leaf disease using Deep learning

ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.25912/RDF_1670808596168&rft.title=Image dataset for detecting sugarcane white leaf disease using Deep learning&rft.identifier=10.25912/RDF_1670808596168&rft.publisher=Queensland University of Technology&rft.description=This work applied remote sensing techniques based on unmanned aerial vehicles (UAVs) and deep learning (DL) to detect WLD in sugarcane fields at the Gal-Oya Plantation, Sri Lanka. The established methodology to detect WLD consists of UAV red, green, and blue (RGB) image acquisition, the pre-processing of the dataset, labelling, DL model tuning, and prediction. Acknowledgements: Narmilan Amarasingam conducted the UAV flight mission, and analysis and prepared the manuscript for final submission as a corresponding author. Felipe Gonzalez, Kevin Powell, and Juan Sandino provided overall supervision and contributed to the writing and editing. Surantha provided the technical guidance to conduct the UAV flight mission and research design and provided feedback on the draft manuscript. &rft.creator=Narmilan, Amarasingam &rft.date=2022&rft.edition=1&rft.relation=https://eprints.qut.edu.au/235559/&rft.coverage=81.675582,7.223780&rft_rights=© Narmilan Amarasingam, 2022. &rft_rights=Creative Commons Attribution-NonCommercial-No Derivatives 3.0 http://creativecommons.org/licenses/by-nc-nd/4.0/&rft_subject=Machine learning&rft_subject=Remote sensing&rft_subject=White leaf disease&rft_subject=Convolutional neural networks&rft_subject=Sugarcane;&rft_subject=Object detection&rft_subject=Precision agriculture&rft.type=dataset&rft.language=English Access the data